So, you’ve learned the skills needed to become a data analyst. You can write queries to retrieve data from a database, scour through user behavior to discover rich insights, and interpret the complex results of A/B tests to make substantive product recommendations.
In short, you feel confident about embarking full steam ahead on a career as a data analyst. The next question is, how do you get noticed and actually hired by recruiters or hiring managers?
There are three main steps you should take in your master plan for data analysis domination: 1. Build data science projects; 2. Show your work and make it publicly available; 3. Network, network, and network some more.
Luckily, data analyst jobs are extremely abundant, lucrative, and intellectually fulfilling. There’s no shortage of work, and good work at that—it’s just a question of how to find it and earn it.
Build Data Science Projects
Building projects is a great way to apply and showcase the data analysis skills that you’ve added to your arsenal. It’s also a solid opportunity for you to demonstrate that you can work through a data problem end-to-end: from data acquisition and cleaning through analysis, to communicating your findings so clearly that even the tech-illiterate can follow along.
Where to start? To begin with, you can tap into projects from data science classes as inspiration. For example, Harvard’s CS109 Data Science Class makes its lectures available (for free!) via online video. You can also browse through student projects from the class on YouTube.
Got your brain juices flowing, but still not sure what project to tackle? Narrow in on a specific task or question you’re interested in solving. For example, there are lots of socially relevant data sets available online that you can analyze. Here are three specific examples of rich data pools you can pull from to examine data on a global, national, and hyperlocal level:
- World Bank: The World Bank Open Data project provides free and open access to thousands of data sets about development in countries around the globe. You can browse by country, by topic (for instance, Energy & Mining, Climate Change, or External Debt), or by indicator (like labor force participation rate, military expenditure, or life expectancy at birth). Here’s a freebie: Compare post-earthquake metrics for the relief efforts in Haiti with those of Pakistan.
- U.S. Census: The U.S. Census provides ample potential for fascinating data insights. Slice and dice data sets on everything from population estimates per square mile to mean travel time to work. Want more? Infochimps also has a great set of free APIs focused on census data. Census data is really great for conducting spatial analysis. For example, you could compare the average level of education to mean household income for all U.S. zipcodes and display the results on a browseable map.
- Local data: New York, San Francisco, Seattle, Philadelphia, and other cities have all made some subset of their city data publicly available, from public transportation and energy usage to school test scores and crime. Check to see if your city, or one nearby, maintains an open data repository and have at it.
Want even more? Check out this master list of interesting data sets found by some of the best and most well known data scientists in the field today.
Another tactic for building up your portfolio of data science projects is participating in a data competition. For example, try your hand at a Kaggle competition. It’s a great way to gauge your abilities against those of your peers; and if you do well (i.e., place in the top 10), you’ll have another arrow in your quiver in the search for plumb data analyst jobs. You could even land an interview from the brand that sponsored the contest. Companies hiring data analysts are known to search the Kaggle leaderboards when hiring.
Lena Vayn, Head of Talent at startup Soldsie, confirmed that plugging away at data projects is a solid way to look good to recruiters and hiring managers: “It’s a great way to showcase your work and also learn MySQL and even some Python, depending on what kind of data role you want.”
Jake Perlman-Garr, Co-Founder of Datavore Labs, seconded the suggestion: “While you may not currently possess a job helping to build your data analysis skill set, intellectual curiosity is important. There’s plenty of public data available, so pick something that interests you and start to dig down into it.”
Show Your Work, Publicly
Speaking of establishing a data science portfolio, a crucial way to attract the notice of data analyst recruiters is to show and tell. Specifically, showcase your skills and projects on GitHub, or a personal site constructed through Jekyll, WordPress, Medium, Tumblr, SquareSpace, or another personal blog platform.
A strong data portfolio should illustrate your range, including hands-on experience with R, Pandas, Numpy, Scipy, Scikit-Learn, or related data analysis tools; experience working with, and wrangling, very large (too big to fit into one spreadsheet) or unstructured data sets; knowledge of machine-learning and data-mining techniques; and strong problem solving, math, statistics, and quantitative reasoning skills.
Companies hiring data analysts are known to search the Kaggle leaderboards when hiring.
Tarush Aggarwal, who heads up the data engineering team at Offerpop, has seen from his experience that since data analysis differs from company to company, it’s crucial to nurture a broad skill set and then demonstrate those abilities in discoverable ways. “Each company requires their own customized solution for their use cases,” he said. “It’s better to gain a broader education in the data sphere rather than starting out focused only on one technology. Play around with as much as you can.”
The work you share should also demonstrate stellar communication skills. It’s all well and good if you can analyze exceedingly complex data and dig up interesting insights from it. But if you can’t relay those findings in a coherent way, in the correct business context, your skills will be of no use to an organization.
Network, Network, Network
Your Rolodex is your most powerful tool in your hunt for good work. Truly, sometimes it’s who you know, rather than what you know, that can land you the dream job. And having the right professional network at your fingertips can expose you to more job opportunities than if you were trying to land a gig alone.
Perlman-Garr confirmed that notion: “I can speak from experience, as all of my company’s early data-focused hires have come from our networks and the NYC technology community.”
A few good ways to build up your network of professional contacts:
- Attend local data science meetups. They’re great opportunities to log face time with others in the industry who may hold positions you’d like to attain or else who know people who do. In addition, the people you meet may know of companies hiring for positions that you’re qualified for.
- Reach out to other data analysts or data scientists on LinkedIn. Ask them relevant questions about their work, and ask what advice they’d give aspiring data analysts on finding or getting a job (just be courteous and appropriate, couching your “cold call” in the understanding that they are likely very busy and that you sincerely appreciate their time).
- Answer questions in popular digital communities like Quora and Cross Validated in order to build your credibility and your online footprint. Many data professionals, as well as data recruiters and hiring managers, frequent those sites, and your posts and answers may impress them.
Aggarwal advised, “Speak to as many data analysts as possible from a diverse list of companies across industries, and identify what challenges they face and what solutions have worked or not worked for them in the past. Don’t be afraid to ask questions, it’s not a sign of weakness in any way.”
Also remember that professional relationships are a two-way street. Sometimes the best way to ensure you’ll get the most out of one is to do the assistance yourself, first: doing someone a favor by making an introduction or offering to review their e-book on Amazon, for example, is money in the networking bank for the future.
Even when you’ve got a job, if you’re not in love with it, networking can enable you to figure out exactly what you want to do, and then shift gears to something that’s a better fit. “One option is to join a larger organization where you can work your way into a role that interests you,” said Vayn. “Or join a smaller company to run data across a multitude of projects until realizing what sort of data analysis you truly want to do: build the actual processes and systems, or run queries and build recommendations.”
The Bottom Line
With data analysis experience under your belt, it’s time to put your skills to the test by putting yourself on the market as a data analyst for hire. This three-pronged approach—building personal projects, showcasing your work, and networking—will demonstrate both that you can do the work required of a data analyst and that you’re available to do so.